Efficient, cell type-selective delivery of genetic payloads remains a central challenge in the development of gene and cell therapies. Lipid nanoparticles (LNPs) offer a versatile delivery platform, but their optimization is hindered by reliance on brute-force screening methods that are laborious, resource-intensive, and focus on single targets. Here, we present FALCON (Framework for Active Learning-driven Compositional Optimization of Nanoparticles), a closed-loop pipeline that leverages iterative screening, surrogate modeling, and multi-objective optimization to accelerate LNP compositional design. In B cell-targeted validation experiments, FALCON-optimized LNPs achieved a 1.8-fold increase in splenic B cell transfection in vivo compared with reference compositions. When optimized for selectivity, FALCON LNPs displayed an 84-fold improvement in selective transfection of splenic B cells over off-target liver populations and enabled spleen-tropic behavior across factorial panels of varying ionizable and helper lipid chemistries. In vaccine studies, these LNPs induced higher IgG2c antibody titers and a more Th1-biased immune profile. FALCON was also deployed to optimize LNPs for myeloid cell-selective delivery, achieving enhanced in vivo selectivity following systemic administration both across and within spleen and liver compartments. Our results establish FALCON as a useful tool for data-driven design of LNP compositions for precision gene delivery.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 0
- Comments 0